Severe symptoms predict salivary interleukin-6, interleukin-1β, and tumor necrosis factor-α levels in children and youth with obsessive-compulsive disorder
Bibliographic record
Abstract
OBJECTIVE: Childhood-onset obsessive-compulsive disorder (OCD) has been associated with immune dysregulation, including aberrant plasma inflammatory markers and increased rates of infectious and immune-mediated disorders. Saliva may provide a minimally-invasive tool for assessing oral mucosal immunity and inflammatory biomarkers in this population. The primary aim of this study was to compare salivary defense proteins and inflammatory mediators in saliva from children and youth with OCD and healthy controls, and evaluate their associations with measures of oral health and OCD phenotype. METHODS: In this cross-sectional observational study, saliva was collected from 41 children and youth with childhood-onset OCD and 46 healthy controls. Levels of lysozyme, α-amylase, secretory immunoglobulin A (sIgA), C-reactive protein (CRP), interleukin-6 (IL-6), IL-1β, and tumor necrosis factor-α (TNF-α) were quantified by enzyme-linked immunosorbent assays or electrochemiluminescent-based immunoassays. RESULTS: All analytes were detectable in saliva. When adjusting for salivary flow rate and total protein, multiple linear regression models including demographic variables, oral health measures, and OCD status explained a significant proportion of the variance in IL-6, IL-1β, and sIgA but not TNF-α, CRP, α-amylase, or lysozyme levels. Diagnosis of OCD was associated with significantly higher IL-6 (β = 0.403, p = 0.026), while severity of OCD was a significant predictor of increased cytokines (IL-6, β = 0.325, p = 0.009; IL-1β, β = 0.284, p = 0.020; TNF-α, β = 0.269, p = 0.036), but not other analytes. CONCLUSION: These data point to the feasibility of analyzing soluble immune mediators in the saliva in childhood-onset OCD, suggesting that pro-inflammatory cytokines are associated with OCD diagnosis and symptom severity. Further work is required to elucidate the factors contributing to this association and implications for clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".